is there numpy.asarray like function in pandas?

I wanna convert a nd array or some columns to pandas temporarily to use its functionality, so I do not wanna copy data, I wanna only copy view like numpy.asarray.

According to the documentation (https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html), you can pass `copy=False` in the constructor. That's already the default argument, so you shouldn't need to do anything.

This follows the behavior of `numpy` arrays (well, the default argument is `True` for numpy rather than `False`. Pandas can't be that consistent), and in fact the definition for `asarray` uses this same approach:

``````def asarray(a, dtype=None, order=None):
"""Convert the input to an array...
"""
return array(a, dtype, copy=False, order=order)
``````

Numpy documentation states that

copy : bool, optional If true (default), then the object is copied. Otherwise, a copy will only be made if array returns a copy, if obj is a nested sequence, or if a copy is needed to satisfy any of the other requirements (`dtype`, `order`, etc.).

And despite pandas stating

copy : boolean, default False Copy data from inputs. Only affects DataFrame / 2d ndarray input

it will affect the behavior of 1d arrays, and will copy if the `dtype` argument does not match the underlying data.

An example:

``````In [10]: t = np.arange(10)

In [11]: p = pd.DataFrame(t)

In [12]: t
Out[12]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

In [13]: p
Out[13]:
0
0  0
1  1
2  2
3  3
4  4
5  5
6  6
7  7
8  8
9  9

In [14]: t[:3] = 4

In [15]: t
Out[15]: array([4, 4, 4, 3, 4, 5, 6, 7, 8, 9])

In [16]: p
Out[16]:
0
0  4
1  4
2  4
3  3
4  4
5  5
6  6
7  7
8  8
9  9
``````